Instructions to use vubacktracking/mamba_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vubacktracking/mamba_text_classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vubacktracking/mamba_text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vubacktracking/mamba_text_classification: direct link, hf CLI and curl.
- Browser
- Download file 517 MB
-
https://huggingface.co/vubacktracking/mamba_text_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vubacktracking/mamba_text_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vubacktracking/mamba_text_classification/resolve/main/pytorch_model.bin
517 MB
- Xet hash:
- f9a8139e04f6fa2306861b8b39b295e6afe8c0373794ff4f0a768138858b02c8
- Size of remote file:
- 517 MB
- SHA256:
- b2568c8e6db595985e62ce9c1c0fec6b8d5370dfa14500ffae9e559e4403ee44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.